Search Results (906)

The soundscape serves as a critical determinant of the quality of urban wetland parks. This study employs a mixed-methods approach to comprehensively evaluate wetland soundscapes. First, field investigations combining sound level measurements and questionnaire surveys were conducted in Aixi Lake Wetland Park to analyze the spatiotemporal characteristics of the soundscape. Second, laboratory-based physiological tracking (using wearable sensors) and cognitive tests (Sustained Attention to Response Task, SART) were utilized to experimentally quantify the restorative benefits of typical soundscapes. The findings reveal that: (1) sound level indicators and sound harmonious degree in urban wetland parks exhibit significant spatiotemporal characteristics and distributional variations; (2) a marked competitive effect among biological, geophysical, and human activity sounds is observed in their spatial distribution; sound harmonious degree demonstrates significant spatial autocorrelation in both global and local models; (3) different sound sources possess varying restorative potentials, with bird song showing the highest restorative effect; the SHDs of biological and geophony, along with L_Aeq, are key factors affecting PRSS; (4) a positive correlation exists between L_Aeq and the PRSS up to 56.4 dB, beyond which PRSS declines with increasing L_Aeq; (5) at the physiological level, short-term exposure to urban wetland park soundscapes can rapidly alleviate stress, with the most pronounced restorative effects occurring within the first 60 s; and (6) in terms of attention, soundscape stimulation reduces SART response times and improves response speed, while bird song from treetops and musical sounds further decrease response errors. Full article

Noise disturbances can cause conflicts in several areas, such as residences, civil constructions, highways, subways, and airports, measured by different scales of acoustic comfort for community well-being evaluation. These disturbances also have signatures such as frequency, amplitude, and temporal patterns to compare acoustic comfort with real-time parameters. In addition, acoustic sensors should be chosen based on accuracy, price, and calibration method, and acoustic insulation should be applied with the aim of achieving reliable measurements in indoor and outdoor environments for sustainable urban living. In some situations, the lack of noise control can lead to several human disorders, from hearing loss to cardiovascular complications. Therefore, legislation and regulation should be carefully studied and applied to achieve an equilibrium between energy-efficient and healthy building designs in entertainment, work, and rest activities with measured parameters visualized through the design of interface tools that should enable the collection and organization of sound data, with proper presentation for the final user. Finally, intellectual property registrations bring recent industrial applications with aspects of noise mitigation. All these features constitute noise disturbance mitigation in a multi-dimensional integration framework of technology, health, and law to improve the quality of life in human-centric environments. Full article

This paper presents a miniaturized and highly accurate saltwater concentration monitoring system based on Capacitive Micromachined Ultrasonic Transducer (CMUT) array technology. The system incorporates a highly integrated CMUT array with a compact footprint of 5 mm × 5 mm, capable of both transmitting and receiving ultrasonic signals, which significantly contributes to the system’s miniaturization and portability. To ensure accurate compensation for temperature-dependent variations in sound velocity, a TA610A temperature sensor is integrated for continuous real-time monitoring of the salt solution temperature. By acquiring ultrasonic echo signals, the system calculates the time-of-flight (TOF) of the acoustic waves. Based on the TOF and real-time temperature data, the sound velocity is determined, and the salt concentration is subsequently derived with temperature compensation applied to enhance measurement accuracy. Experimental results show a measurement precision of 0.1% and a maximum absolute error of 0.02%, confirming the system’s high accuracy and robustness. Combining stability, reliability, and a compact real-time sensing design, the proposed CMUT-based system holds significant promise for practical deployment in various industrial and environmental monitoring scenarios. Full article

Unlike terrestrial environments, the inhomogeneity distribution of underwater sound speed poses significant challenges for underwater ranging and target localization. In the presence of sound ray bending and sensor node position errors in underwater acoustic sensor networks (UASNs), this paper proposes a joint TOA-AOA deep-sea target localization framework based on sound ray bending correction. From the perspective of information theory and time series analysis, the TOA measurements are time series signals carrying target position information, and the entropy-based analysis quantifies the fundamental limit on localization uncertainty. First, based on the TOA time series measurements and combined with the acoustic propagation characteristics of the deep sea, a sound ray bending correction method is adopted to improve the accuracy of slant range measurement. To enhance target localization accuracy, this paper proposes a two-step WLS closed-form solution based on TOA-AOA. To further reduce localization bias, a maximum likelihood estimation (MLE) method based on the Gauss-Newton is also derived. Subsequently, the paper derives and analyzes the Cramér-Rao lower bound (CRLB) for target localization, proving theoretically that jointly using TOA-AOA can improve localization accuracy. Simulations verify the performance of the proposed methods. The slant range estimation method based on sound ray bending correction effectively improves range measurement accuracy. The proposed closed-form solution enhances target localization accuracy, achieving the CRLB accuracy. The Gauss-Newton MLE solution can attain the CRLB accuracy under certain localization geometries and further reduces localization bias. Full article

Negative driving emotions constitute a significant factor compromising road safety. Current intelligent vehicle human machine interaction (HMI) systems predominantly focus on functional implementation, lacking the capability to perceive and adapt to the driver’s psychological state. To address this issue, this study investigates the intrinsic relationship between driving emotions and HMI through multimodal experiments. Experiment One reveals the distribution patterns of drivers’ visual attentional scope under different emotional states. Experiment Two establishes a color preference model for HMI interfaces corresponding to specific emotions. Experiment Three quantitatively analyzes the impact of emotional variations on the perceptual efficiency of auditory warnings. Based on the experimental data, an interaction design principle matching “Emotion-Scene-Modality” is formulated, guiding the design of a data-driven, emotion-adaptive HMI prototype system. This system can perceive the driver’s emotional state in real time via multimodal sensors and dynamically adjust interface color themes, information layout, warning sound effects, and voice interaction style according to predefined interaction strategies. Usability testing demonstrates that, compared to traditional static HMI, this affective adaptive system effectively mitigates the driver’s negative emotional load and provides alerts that are more perceptible and less likely to cause irritation during critical moments. Consequently, it offers a significant theoretical foundation and practical reference for constructing a safer and more comfortable next-generation intelligent vehicle cockpit interaction paradigm. Full article

This paper proposes a new Unified Namespace (UNS)-based architecture to improve predictive and prescriptive maintenance of industrial equipment and overcome challenges such as incomplete data, poor interoperability, and disconnected IT/OT environments. The framework combines interoperable data formats in real-time sensor data, predictive modeling, prescriptive analytics, and simulations of digital twins, using UNS as a centralized, protocol-agnostic data layer that is scalable and complies with Industry 4.0 and Pharma 4.0 standards. The suggested methodology increases data accessibility, reduces integration complexity, and allows low-latency analytics and automated decision-making. Machine learning predictive models achieved more than 94% accuracy in predicting equipment failures. Prescriptive analytics provides maintenance recommendations to reduce downtime and risks. The feedback loops of digital twins can enhance the accuracy of predictions and allow decision optimization through what-if analysis. A test-bench deployment showed a higher performance compared to traditional point-to-point integration, with lower latency (approximately 18 ms vs. approximately 31 ms), decreasing packet loss (0.40% vs. 3.11%), and higher model accuracy (94.20% vs. 87.51%). The structure avoided more than 4000 simulated breakdowns in the test-bench environment, indicating dependability. The study connects the theoretical applications of the UNS with the actual maintenance processes and provides a sound approach to the industrial analytics and optimization of the equipment. Full article

Aircraft noise monitoring systems deployed at major airports typically rely on scalar energy-based indicators, which primarily describe integrated sound energy but provide limited representation of the spectral–temporal structure and perceptual attributes of aircraft noise. To address this limitation, this study proposes a sensor-based psychoacoustic feature extraction and spatiotemporal analysis framework for continuous aircraft noise monitoring under high-density operational conditions. An automatic noise monitoring system compliant with ISO 20906 was deployed to synchronously acquire acoustic waveforms and ADS-B trajectory data. A cascaded spatiotemporal fusion algorithm was developed to associate noise events with aircraft flight paths, followed by a model-stratified multidimensional IQR-based data cleaning strategy to suppress environmental interference and non-stationary outliers. Based on the cleaned dataset, a suite of psychoacoustic features—including loudness, sharpness, roughness, fluctuation strength, and tonality—was extracted to characterize the perceptual structure of aircraft noise beyond conventional energy metrics. Experimental results demonstrate that, under equivalent sound exposure levels, psychoacoustic features retain substantial discriminative information that is lost in scalar energy indicators. The coefficients of variation for fluctuation strength and tonality reach 43.2% and 22.1%, respectively, corresponding to 15–69 times higher sensitivity compared to traditional energy-based metrics. Furthermore, nonlinear manifold mapping using UMAP reveals clear topological separation between new-generation and legacy aircraft models in the psychoacoustic feature space, whereas severe overlap persists in energy-based representations. Correlation analysis further indicates decoupling between macro-level physical design parameters (e.g., bypass ratio, thrust) and perceptual feature dimensions, highlighting the limitations of energy-centric monitoring schemes. The proposed framework demonstrates the feasibility of integrating psychoacoustic feature extraction into continuous sensor-based aircraft noise monitoring systems. It provides a scalable signal processing pipeline for enhancing the resolution and interpretability of aircraft noise measurements in complex operational environments. Full article

The upper atmospheric microwave sounding channels data are important for atmospheric data assimilation and retrieval. However, radiative transfer simulation accuracy is constrained by the precise characterization of the Zeeman splitting effect. This study investigates key influencing factors in upper-atmospheric microwave radiance simulations, focusing on the geomagnetic field parameters and the Zeeman splitting absorption coefficients. A three-dimensional (3D) atmosphere-magnetic coupling dataset is constructed using the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) version 2.0 Level 2A atmospheric profiles and the International Geomagnetic Reference Field (IGRF-13) as input for the microwave Line-by-Line (LBL) model. Observations from Special Sensor Microwave Imager/Sounder (SSMIS) channels 19 and 20 are used to quantitatively compare the effects of 2D and 3D geomagnetic fields on simulations and evaluate the impact of updated Zeeman splitting coefficients. Quantitative analysis reveals that the average vertical attenuation rate of geomagnetic field strength between 50 and 0.001 hPa is 2.98%, and using 3D magnetic field parameters improves the observation and simulation bias (O-B) for SSMIS channels 19 and 20 by approximately 3.67% and 3.52%, respectively. The updated microwave LBL model, incorporating molecular self-spin interactions and higher-order Zeeman effects, reduces the mean absolute error (MAE) and root mean square error (RMSE) of the SSMIS channel 20 by approximately 2.7% and 2.25%, respectively. Experimental results indicate that the 7⁺ line within a 2 MHz frequency shift is sensitive to moderate magnetic field strength (0.35–0.55 Gauss), while the 1⁻ line is sensitive to strong magnetic fields (0.5–0.7 Gauss). This study demonstrates that optimizing geomagnetic field representation and Zeeman splitting coefficients can improve upper atmospheric microwave radiance simulation accuracy by detailed comparison with observations. Full article

Real-time lithology identification while drilling is widely applied in oil and gas exploration, development drilling, geo-steering, unconventional resource extraction, well logging, and environmental monitoring, enhancing efficiency and accuracy in subsurface operations. This study investigates the frequency characteristics of rock-drilling sounds generated during drilling operations and explores their potential for real-time lithology identification. Experiments were conducted using 8 mm and 14 mm drill bits at both high and low rotational speeds on four types of rock samples: sandstone, limestone, granite, and shaly sandstone. Sound signals were recorded both within the rock and in air using high-fidelity sensors. The results reveal distinct frequency patterns for each rock type, with sandstone exhibiting dominant low-frequency energy, limestone and granite showing broader frequency bands with strong high-frequency components, and shaly sandstone displaying a mix of low- and high-frequency energy. Quadratic polynomial regression models between the Vp or Vs and the peak frequencies of the four distinct rock samples are built, and the corresponding coefficients of determination are 0.9878 and 0.9799. The study also demonstrates that drilling parameters, such as drill bit diameter and revolutions per minute (RPM), significantly influence the frequency distribution of rock-drilling sounds, with larger drill bits and higher RPMs producing broader frequency bands and stronger high-frequency energy. Comparisons between in-rock and in-air recordings show that the latter captures richer high-frequency information, though the overall trends remain consistent. These findings provide an experimental foundation for using rock-breaking sounds as a potential tool for lithology identification during drilling operations. The study highlights the importance of considering rock heterogeneity and drilling conditions when interpreting acoustic data and suggests future work to validate the method in field conditions and integrate advanced data processing techniques. Full article

Spinning tethered satellite systems represent a promising advancement in the design of spaceborne architectures for Earth and planetary observation. Leveraging the unique advantages of tether technology, such as mass efficiency in deploying large structures and fuel-free formation control, this study explores the feasibility and performance potential of CubeSat-scale spinning tethered formations. These systems consist of multiple spacecrafts connected by a tether, enabling easy dynamic adjustment of inter-satellite spacing and rotational velocity through conservation of angular momentum. Such flexibility facilitates precise, stable formations suitable for a range of remote sensing applications. In this paper, the authors present an overview of the dynamical modelling, deployment strategy, and operational advantages of spinning tether systems, focusing in particular on some key use cases: Earth, Moon and Mars surface observation. Three representative sensing modalities are analysed: (1) stereo imaging, where tethered platforms allow synchronized capture with tuneable baselines; (2) distributed radar sounding, which benefits from mechanically stabilized, spatially dispersed sensors to enhance resolution; and (3) Synthetic Aperture Radar (SAR) interferometry, where tether-induced baseline control improves accuracy and simplifies phase unwrapping. A performance assessment is provided for multiple orbital configurations around the Earth and the Moon. The results demonstrate that, while some issues still need to be explored in more detail, spinning tethered systems can offer competitive or superior observational performance in different mission scenarios compared to current technologies. The main challenges posed by this kind of architecture are discussed, alongside future research directions and development prospects. Full article

Continuous respiratory monitoring plays a crucial role in both clinical and non-clinical applications, providing valuable insights into physiological health. This paper presents a sustainable, contactless respiratory monitoring framework that integrates acoustic sensing with a lightweight convolutional neural network (CNN) optimized for low-power embedded platforms. Breathing sounds are processed using wavelet-based denoising and Mel-Frequency Cepstral Coefficient (MFCC) extraction, achieving 94.8% classification accuracy with an inference latency of 0.3 s per frame. The quantized model deployed on a Sony Spresense microcontroller reduces memory usage by over 90%. By eliminating disposable sensors and minimizing energy consumption, the proposed approach delivers an eco-efficient, scalable, and real-time solution for continuous respiratory assessment. Full article

Heart and respiratory rate monitoring during sleep enables the detection of physiological irregularities through contact or contactless methods. Traditional approaches like polysomnography are accurate but costly, ergonomically limited, and often poorly accepted by patients. Smart Bedding^® is a novel, flexible bedsheet equipped with a high-resolution sensor network that records movement, pressure, sound, temperature, and humidity throughout the night. This study aimed to estimate cardiorespiratory parameters using the Smart Bedding^® IMU. Data from 30 participants sleeping on Smart Bedding^® while undergoing simultaneous polysomnography were analyzed. A robust and low-cost preprocessing pipeline was developed; estimation was performed using zero-crossing, peak detection, and Burg’s method for comparison, and validation was conducted using polysomnography as the gold-standard reference. Respiratory and heart rates were accurately estimated, achieving overall accuracies of 93.9% and 88.7% using zero-crossing and peak detection, respectively. Respiratory rate estimation showed no significant limitations across the frequency spectrum or among sleeping positions. However, heart rate estimation accuracy decreased when the frequency was below 55 BPM or when participants slept in a lateral sleep position, likely due to reduced cardiac signal power. Overall, the proposed methodology accurately tracked respiratory and cardiac patterns throughout the night, supporting Smart Bedding^® as a promising tool for future sleep tracking applications. Full article

Rock geotechnical properties can be reflected in drill signals while drill rod penetrates through rocks. The rate of penetration, rotary speed, torque, load, sound, vibration, etc., are different for various rock types, since they are influenced by rock properties. Therefore, a close analysis and derivations of these drill signals can provide valuable insights into rock geotechnical properties. The drill returned signals from the mechanical sensors; for example, torque and load are commonly interpreted to characterize the rock properties. There are still limitations to such sensors and interpretation methodologies that can confidently characterize rock properties. In this research, mechanical sensors were compared and complemented with seismic sensors, for example, accelerometers and geophones, to characterize rocks and interfaces. This paper presents experimental results conducted with synthetic rock samples using mechanical and seismic sensors with a field scale drilling machine. The results show that seismic sensors can identify voids or weak (fractured) interfaces clearly compared to mechanical sensors. Smaller gaps have smaller span of low frequency and vice versa. The sensors attached to the drill head were less sensitive than the sensors attached to the sample. Drill signals showed the capacity to effectively identify material interfaces and weak fractures up to 4 mm thick, with geophones providing clearer data than accelerometers. Neither sensor distinguished fractured zones from voids. Sensors mounted directly on the sample were more sensitive than those attached to the drill head, likely due to vibration-induced signal attenuation at the drill head. Full article

Accurate and energy-efficient localization of autonomous underwater vehicles (AUVs) remains a fundamental challenge due to the complex, bandwidth-limited, and highly dynamic nature of underwater acoustic environments. This paper proposes a fully adaptive deep reinforcement learning (DRL)-driven localization framework for AUVs operating in Underwater Acoustic Sensor Networks (UAWSNs). The localization problem is formulated as a Markov Decision Process (MDP) in which an intelligent agent jointly optimizes beacon selection and transmit power allocation to minimize long-term localization error and energy consumption. A hierarchical learning architecture is developed by integrating four actor–critic algorithms, which are (i) Twin Delayed Deep Deterministic Policy Gradient (TD3), (ii) Soft Actor–Critic (SAC), (iii) Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and (iv) Distributed DDPG (D2DPG), enabling robust learning under non-stationary channels, cooperative multi-AUV scenarios, and large-scale deployments. A round-trip time (RTT)-based geometric localization model incorporating a depth-dependent sound speed gradient is employed to accurately capture realistic underwater acoustic propagation effects. A multi-objective reward function jointly balances localization accuracy, energy efficiency, and ranging reliability through a risk-aware metric. Furthermore, the Cramér–Rao Lower Bound (CRLB) is derived to characterize the theoretical performance limits, and a comprehensive complexity analysis is performed to demonstrate the scalability of the proposed framework. Extensive Monte Carlo simulations show that the proposed DRL-based methods achieve significantly lower localization error, lower energy consumption, faster convergence, and higher overall system utility than classical TD3. These results confirm the effectiveness and robustness of DRL for next-generation adaptive underwater localization systems. Full article

With the increasing frequency extreme weather events associated with climate change, real-time monitoring of rainfall intensity is critical for water resource management, disaster warning, and other applications. Traditional methods, such as ground-based rain gauges, radar, and satellites, face challenges like high costs, low resolution, and monitoring gaps. This study proposes a novel real-time rainfall intensity monitoring method based on deep learning and audio signal processing, using acoustic features from rainfall to predict intensity. Conducted in the semi-arid region of Northwest China, the study employed a custom-designed sound collection device to capture acoustic signals from raindrop-surface interactions. The method, combining multi-feature extraction and regression modeling, accurately predicted rainfall intensity. Experimental results revealed a strong linear relationship between sound pressure and rainfall intensity (r = 0.916, R² = 0.838), with clear nonlinear enhancement of acoustic energy during heavy rainfall. Compared to traditional methods like CML and radio link techniques, the acoustic approach offers advantages in cost, high-density deployment, and adaptability to complex terrain. Despite some limitations, including regional and seasonal biases, the study lays the foundation for future improvements, such as expanding sample coverage, optimizing sensor design, and incorporating multi-source data. This method holds significant potential for applications in urban drainage, agricultural irrigation, and disaster early warning. Full article